Overview

Dataset statistics

Number of variables18
Number of observations166795
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.9 MiB
Average record size in memory144.0 B

Variable types

Numeric16
Categorical2

Alerts

City is highly overall correlated with CountyHigh correlation
Clean Alternative Fuel Vehicle Eligibility is highly overall correlated with Electric Range and 1 other fieldsHigh correlation
County is highly overall correlated with City and 1 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle Eligibility and 2 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle Eligibility and 1 other fieldsHigh correlation
Model is highly overall correlated with VIN (1-10)High correlation
Model Year is highly overall correlated with Electric RangeHigh correlation
Postal Code is highly overall correlated with CountyHigh correlation
VIN (1-10) is highly overall correlated with ModelHigh correlation
State is highly skewed (γ1 = -27.56089108)Skewed
Postal Code is highly skewed (γ1 = -30.09608692)Skewed
2020 Census Tract is highly skewed (γ1 = -26.96677648)Skewed
DOL Vehicle ID has unique valuesUnique
Electric Range has 83517 (50.1%) zerosZeros
Base MSRP has 163433 (98.0%) zerosZeros

Reproduction

Analysis started2024-03-11 03:39:52.080121
Analysis finished2024-03-11 03:41:24.155860
Duration1 minute and 32.08 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

VIN (1-10)
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.79518
Minimum1
Maximum1114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:24.350677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median52
Q3196
95-th percentile1029
Maximum1114
Range1113
Interquartile range (IQR)179

Descriptive statistics

Standard deviation269.25387
Coefficient of variation (CV)1.5857568
Kurtosis5.0768332
Mean169.79518
Median Absolute Deviation (MAD)43
Skewness2.4018884
Sum28320987
Variance72497.646
MonotonicityNot monotonic
2024-03-11T03:41:24.649773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 2904
 
1.7%
4 2748
 
1.6%
3 2730
 
1.6%
5 2690
 
1.6%
2 2620
 
1.6%
11 2519
 
1.5%
10 2500
 
1.5%
7 2499
 
1.5%
8 2496
 
1.5%
12 2484
 
1.5%
Other values (249) 140605
84.3%
ValueCountFrequency (%)
1 1968
1.2%
2 2620
1.6%
3 2730
1.6%
4 2748
1.6%
5 2690
1.6%
6 2904
1.7%
7 2499
1.5%
8 2496
1.5%
9 2439
1.5%
10 2500
1.5%
ValueCountFrequency (%)
1114 1114
0.7%
1090 1090
0.7%
1071 2142
1.3%
1052 1052
0.6%
1041 1041
0.6%
1037 1037
0.6%
1029 1029
0.6%
1022 1022
0.6%
1014 1014
0.6%
993 993
0.6%

County
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49568.435
Minimum1
Maximum86594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:24.971640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile943
Q19847
median86594
Q386594
95-th percentile86594
Maximum86594
Range86593
Interquartile range (IQR)76747

Descriptive statistics

Standard deviation38769.326
Coefficient of variation (CV)0.78213738
Kurtosis-1.9294575
Mean49568.435
Median Absolute Deviation (MAD)0
Skewness-0.12137205
Sum8.2677672 × 109
Variance1.5030607 × 109
MonotonicityNot monotonic
2024-03-11T03:41:25.267751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
86594 86594
51.9%
19570 19570
 
11.7%
12972 12972
 
7.8%
9847 9847
 
5.9%
6042 6042
 
3.6%
5522 5522
 
3.3%
4312 4312
 
2.6%
4039 4039
 
2.4%
2028 2028
 
1.2%
1842 1842
 
1.1%
Other values (39) 14027
 
8.4%
ValueCountFrequency (%)
1 86
0.1%
2 54
< 0.1%
3 27
 
< 0.1%
4 40
< 0.1%
5 20
 
< 0.1%
6 24
 
< 0.1%
7 14
 
< 0.1%
8 16
 
< 0.1%
9 18
 
< 0.1%
10 10
 
< 0.1%
ValueCountFrequency (%)
86594 86594
51.9%
19570 19570
 
11.7%
12972 12972
 
7.8%
9847 9847
 
5.9%
6042 6042
 
3.6%
5522 5522
 
3.3%
4312 4312
 
2.6%
4039 4039
 
2.4%
2028 2028
 
1.2%
1842 1842
 
1.1%

City
Real number (ℝ)

HIGH CORRELATION 

Distinct208
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6920.752
Minimum1
Maximum27831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:25.557803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile140
Q1859
median2597
Q36032
95-th percentile27831
Maximum27831
Range27830
Interquartile range (IQR)5173

Descriptive statistics

Standard deviation9606.3751
Coefficient of variation (CV)1.3880537
Kurtosis0.82456682
Mean6920.752
Median Absolute Deviation (MAD)2239
Skewness1.5953469
Sum1.1543468 × 109
Variance92282443
MonotonicityNot monotonic
2024-03-11T03:41:25.849040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27831 27831
 
16.7%
8364 8364
 
5.0%
6032 6032
 
3.6%
5869 5869
 
3.5%
5440 5440
 
3.3%
5028 5028
 
3.0%
4876 4876
 
2.9%
4617 4617
 
2.8%
4058 4058
 
2.4%
3510 3510
 
2.1%
Other values (198) 91170
54.7%
ValueCountFrequency (%)
1 227
0.1%
2 150
0.1%
3 90
 
0.1%
4 88
 
0.1%
5 80
 
< 0.1%
6 60
 
< 0.1%
7 105
0.1%
8 80
 
< 0.1%
9 54
 
< 0.1%
10 60
 
< 0.1%
ValueCountFrequency (%)
27831 27831
16.7%
8364 8364
 
5.0%
6032 6032
 
3.6%
5869 5869
 
3.5%
5440 5440
 
3.3%
5028 5028
 
3.0%
4876 4876
 
2.9%
4617 4617
 
2.8%
4058 4058
 
2.4%
3510 3510
 
2.1%

State
Real number (ℝ)

SKEWED 

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.954735
Minimum0
Maximum40
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:26.146338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39
Q139
median39
Q339
95-th percentile39
Maximum40
Range40
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.151665
Coefficient of variation (CV)0.029564184
Kurtosis786.2203
Mean38.954735
Median Absolute Deviation (MAD)0
Skewness-27.560891
Sum6497455
Variance1.3263322
MonotonicityNot monotonic
2024-03-11T03:41:26.458273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
39 166440
99.8%
4 91
 
0.1%
38 38
 
< 0.1%
19 32
 
< 0.1%
36 24
 
< 0.1%
29 14
 
< 0.1%
13 13
 
< 0.1%
5 12
 
< 0.1%
9 10
 
< 0.1%
11 9
 
< 0.1%
Other values (31) 112
 
0.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 1
 
< 0.1%
2 7
 
< 0.1%
3 2
 
< 0.1%
4 91
0.1%
5 12
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 10
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
39 166440
99.8%
38 38
 
< 0.1%
37 3
 
< 0.1%
36 24
 
< 0.1%
35 7
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 6
 
< 0.1%
31 1
 
< 0.1%

Postal Code
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct836
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98173.714
Minimum1730
Maximum99577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:26.777528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1730
5-th percentile98006
Q198052
median98122
Q398371
95-th percentile98942
Maximum99577
Range97847
Interquartile range (IQR)319

Descriptive statistics

Standard deviation2442.5844
Coefficient of variation (CV)0.024880228
Kurtosis954.3603
Mean98173.714
Median Absolute Deviation (MAD)101
Skewness-30.096087
Sum1.6374885 × 1010
Variance5966218.6
MonotonicityNot monotonic
2024-03-11T03:41:27.094852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 4252
 
2.5%
98012 3115
 
1.9%
98033 2840
 
1.7%
98006 2663
 
1.6%
98004 2652
 
1.6%
98115 2553
 
1.5%
98074 2341
 
1.4%
98072 2312
 
1.4%
98188 2286
 
1.4%
98034 2237
 
1.3%
Other values (826) 139544
83.7%
ValueCountFrequency (%)
1730 1
< 0.1%
1731 1
< 0.1%
1824 1
< 0.1%
1908 1
< 0.1%
2842 1
< 0.1%
3804 1
< 0.1%
6355 1
< 0.1%
6371 1
< 0.1%
6379 2
< 0.1%
6385 1
< 0.1%
ValueCountFrequency (%)
99577 1
 
< 0.1%
99403 57
 
< 0.1%
99402 9
 
< 0.1%
99371 1
 
< 0.1%
99362 329
0.2%
99361 12
 
< 0.1%
99360 7
 
< 0.1%
99357 19
 
< 0.1%
99356 1
 
< 0.1%
99354 283
0.2%

Model Year
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.342
Minimum1997
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:27.366945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2014
Q12018
median2021
Q32023
95-th percentile2023
Maximum2024
Range27
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0012851
Coefficient of variation (CV)0.0014855332
Kurtosis0.45023181
Mean2020.342
Median Absolute Deviation (MAD)2
Skewness-1.0860991
Sum3.3698294 × 108
Variance9.0077124
MonotonicityNot monotonic
2024-03-11T03:41:27.638651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2018 14150
 
8.5%
2020 11425
 
6.8%
2019 10859
 
6.5%
2017 8522
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%
2013 4454
 
2.7%
Other values (12) 9317
 
5.6%
ValueCountFrequency (%)
1997 1
 
< 0.1%
1998 1
 
< 0.1%
1999 3
 
< 0.1%
2000 7
 
< 0.1%
2002 2
 
< 0.1%
2003 1
 
< 0.1%
2008 19
 
< 0.1%
2010 23
 
< 0.1%
2011 782
0.5%
2012 1630
1.0%
ValueCountFrequency (%)
2024 3309
 
2.0%
2023 51351
30.8%
2022 27592
16.5%
2021 18774
 
11.3%
2020 11425
 
6.8%
2019 10859
 
6.5%
2018 14150
 
8.5%
2017 8522
 
5.1%
2016 5518
 
3.3%
2015 4833
 
2.9%

Make
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.009353
Minimum0
Maximum38
Zeros29
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:27.908147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median33
Q333
95-th percentile36
Maximum38
Range38
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.153195
Coefficient of variation (CV)0.44596097
Kurtosis-0.82446467
Mean25.009353
Median Absolute Deviation (MAD)3
Skewness-0.87790223
Sum4171435
Variance124.39376
MonotonicityNot monotonic
2024-03-11T03:41:28.204294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
33 74830
44.9%
27 13847
 
8.3%
6 13072
 
7.8%
11 8577
 
5.1%
4 7196
 
4.3%
18 6995
 
4.2%
35 5812
 
3.5%
36 4717
 
2.8%
17 4100
 
2.5%
15 4057
 
2.4%
Other values (29) 23592
 
14.1%
ValueCountFrequency (%)
0 29
 
< 0.1%
1 3464
 
2.1%
2 8
 
< 0.1%
3 3
 
< 0.1%
4 7196
4.3%
5 245
 
0.1%
6 13072
7.8%
7 2878
 
1.7%
8 28
 
< 0.1%
9 801
 
0.5%
ValueCountFrequency (%)
38 3
 
< 0.1%
37 3962
 
2.4%
36 4717
 
2.8%
35 5812
 
3.5%
34 5
 
< 0.1%
33 74830
44.9%
32 788
 
0.5%
31 275
 
0.2%
30 3554
 
2.1%
29 1097
 
0.7%

Model
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14114.946
Minimum1
Maximum32822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:28.534081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile360
Q11919
median6413
Q328925
95-th percentile32822
Maximum32822
Range32821
Interquartile range (IQR)27006

Descriptive statistics

Standard deviation13390.85
Coefficient of variation (CV)0.94870007
Kurtosis-1.656049
Mean14114.946
Median Absolute Deviation (MAD)6053
Skewness0.40754717
Sum2.3543024 × 109
Variance1.7931486 × 108
MonotonicityNot monotonic
2024-03-11T03:41:28.825943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32822 32822
19.7%
28925 28925
17.3%
13273 13273
 
8.0%
7610 7610
 
4.6%
6413 6413
 
3.8%
5428 5428
 
3.3%
4825 4825
 
2.9%
3647 3647
 
2.2%
3161 3161
 
1.9%
3107 3107
 
1.9%
Other values (110) 57584
34.5%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 10
 
< 0.1%
3 6
 
< 0.1%
5 10
 
< 0.1%
6 6
 
< 0.1%
8 8
 
< 0.1%
10 30
< 0.1%
11 22
< 0.1%
12 12
 
< 0.1%
13 26
< 0.1%
ValueCountFrequency (%)
32822 32822
19.7%
28925 28925
17.3%
13273 13273
8.0%
7610 7610
 
4.6%
6413 6413
 
3.8%
5428 5428
 
3.3%
4825 4825
 
2.9%
3647 3647
 
2.2%
3161 3161
 
1.9%
3107 3107
 
1.9%

Electric Vehicle Type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
130288 
1
36507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166795
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Length

2024-03-11T03:41:29.144531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T03:41:29.422187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166795
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common 166795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130288
78.1%
1 36507
 
21.9%

Clean Alternative Fuel Vehicle Eligibility
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
83517 
0
64294 
2
18984 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166795
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Length

2024-03-11T03:41:29.643490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T03:41:29.910678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Most occurring characters

ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166795
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 166795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 83517
50.1%
0 64294
38.5%
2 18984
 
11.4%

Electric Range
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.505057
Minimum0
Maximum337
Zeros83517
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:30.186248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q384
95-th percentile259
Maximum337
Range337
Interquartile range (IQR)84

Descriptive statistics

Standard deviation93.269778
Coefficient of variation (CV)1.5164571
Kurtosis0.38748856
Mean61.505057
Median Absolute Deviation (MAD)0
Skewness1.3744808
Sum10258736
Variance8699.2515
MonotonicityNot monotonic
2024-03-11T03:41:30.484618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83517
50.1%
215 6272
 
3.8%
220 4101
 
2.5%
84 3918
 
2.3%
25 3918
 
2.3%
238 3790
 
2.3%
21 3298
 
2.0%
32 3182
 
1.9%
208 2472
 
1.5%
53 2466
 
1.5%
Other values (92) 49861
29.9%
ValueCountFrequency (%)
0 83517
50.1%
6 935
 
0.6%
8 35
 
< 0.1%
9 21
 
< 0.1%
10 162
 
0.1%
11 3
 
< 0.1%
12 164
 
0.1%
13 358
 
0.2%
14 1109
 
0.7%
15 89
 
0.1%
ValueCountFrequency (%)
337 74
 
< 0.1%
330 318
 
0.2%
322 1671
1.0%
308 485
 
0.3%
293 443
 
0.3%
291 2335
1.4%
289 646
 
0.4%
270 275
 
0.2%
266 1400
0.8%
265 124
 
0.1%

Base MSRP
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1152.1645
Minimum0
Maximum845000
Zeros163433
Zeros (%)98.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:30.784537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8657.8981
Coefficient of variation (CV)7.5144637
Kurtosis603.44483
Mean1152.1645
Median Absolute Deviation (MAD)0
Skewness12.852734
Sum1.9217528 × 108
Variance74959200
MonotonicityNot monotonic
2024-03-11T03:41:31.046187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 163433
98.0%
69900 1390
 
0.8%
31950 381
 
0.2%
52900 220
 
0.1%
32250 141
 
0.1%
54950 130
 
0.1%
59900 124
 
0.1%
39995 111
 
0.1%
36900 100
 
0.1%
44100 95
 
0.1%
Other values (21) 670
 
0.4%
ValueCountFrequency (%)
0 163433
98.0%
31950 381
 
0.2%
32250 141
 
0.1%
32995 3
 
< 0.1%
33950 72
 
< 0.1%
34995 67
 
< 0.1%
36800 54
 
< 0.1%
36900 100
 
0.1%
39995 111
 
0.1%
43700 11
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 10
< 0.1%
110950 20
< 0.1%
109000 6
 
< 0.1%
102000 15
< 0.1%
98950 19
< 0.1%
91250 5
 
< 0.1%
90700 20
< 0.1%
89100 7
 
< 0.1%
81100 22
< 0.1%

Legislative District
Real number (ℝ)

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.187074
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:31.338908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118
median33
Q342
95-th percentile48
Maximum49
Range48
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.838763
Coefficient of variation (CV)0.50840187
Kurtosis-1.0831421
Mean29.187074
Median Absolute Deviation (MAD)11
Skewness-0.46729894
Sum4868258
Variance220.18889
MonotonicityNot monotonic
2024-03-11T03:41:32.964795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 10837
 
6.5%
45 10062
 
6.0%
48 9158
 
5.5%
1 7231
 
4.3%
5 7062
 
4.2%
36 6977
 
4.2%
11 6607
 
4.0%
46 6464
 
3.9%
43 6172
 
3.7%
37 4933
 
3.0%
Other values (39) 91292
54.7%
ValueCountFrequency (%)
1 7231
4.3%
2 1885
 
1.1%
3 824
 
0.5%
4 1385
 
0.8%
5 7062
4.2%
6 1593
 
1.0%
7 787
 
0.5%
8 1726
 
1.0%
9 930
 
0.6%
10 2879
 
1.7%
ValueCountFrequency (%)
49 2250
 
1.3%
48 9158
5.5%
47 3032
 
1.8%
46 6464
3.9%
45 10062
6.0%
44 4337
2.6%
43 6172
3.7%
42 2315
 
1.4%
41 10837
6.5%
40 3637
 
2.2%

DOL Vehicle ID
Real number (ℝ)

UNIQUE 

Distinct166795
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1724184 × 108
Minimum4385
Maximum4.7925477 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:33.467487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile1.0927167 × 108
Q11.7907484 × 108
median2.2440553 × 108
Q32.5134214 × 108
95-th percentile3.4420524 × 108
Maximum4.7925477 × 108
Range4.7925039 × 108
Interquartile range (IQR)72267307

Descriptive statistics

Standard deviation77272387
Coefficient of variation (CV)0.35569753
Kurtosis3.4970115
Mean2.1724184 × 108
Median Absolute Deviation (MAD)30843393
Skewness0.7258894
Sum3.6234853 × 1013
Variance5.9710219 × 1015
MonotonicityNot monotonic
2024-03-11T03:41:33.910100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1593721 1
 
< 0.1%
196311664 1
 
< 0.1%
181452680 1
 
< 0.1%
142435835 1
 
< 0.1%
148905543 1
 
< 0.1%
249787583 1
 
< 0.1%
220480571 1
 
< 0.1%
249691865 1
 
< 0.1%
131525467 1
 
< 0.1%
233740279 1
 
< 0.1%
Other values (166785) 166785
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
24629 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
ValueCountFrequency (%)
479254772 1
< 0.1%
479114996 1
< 0.1%
478935460 1
< 0.1%
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%

Electric Utility
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70131.494
Minimum3
Maximum98811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:34.190497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile29601
Q132454
median98811
Q398811
95-th percentile98811
Maximum98811
Range98808
Interquartile range (IQR)66357

Descriptive statistics

Standard deviation35015.822
Coefficient of variation (CV)0.49928813
Kurtosis-1.606193
Mean70131.494
Median Absolute Deviation (MAD)0
Skewness-0.46860742
Sum1.1697583 × 1010
Variance1.2261078 × 109
MonotonicityNot monotonic
2024-03-11T03:41:34.472907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
98811 98811
59.2%
32454 32454
 
19.5%
29601 29601
 
17.7%
1245 1245
 
0.7%
1059 1059
 
0.6%
989 989
 
0.6%
530 530
 
0.3%
372 372
 
0.2%
355 355
 
0.2%
335 335
 
0.2%
Other values (11) 1044
 
0.6%
ValueCountFrequency (%)
3 3
 
< 0.1%
5 5
 
< 0.1%
6 6
 
< 0.1%
40 40
 
< 0.1%
42 42
 
< 0.1%
56 56
 
< 0.1%
98 98
0.1%
139 139
0.1%
186 186
0.1%
222 222
0.1%
ValueCountFrequency (%)
98811 98811
59.2%
32454 32454
 
19.5%
29601 29601
 
17.7%
1245 1245
 
0.7%
1059 1059
 
0.6%
989 989
 
0.6%
530 530
 
0.3%
372 372
 
0.2%
355 355
 
0.2%
335 335
 
0.2%

2020 Census Tract
Real number (ℝ)

SKEWED 

Distinct2088
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2977092 × 1010
Minimum1.0010201 × 109
Maximum5.6033 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:34.749857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0010201 × 109
5-th percentile5.3011041 × 1010
Q15.303301 × 1010
median5.303303 × 1010
Q35.3053073 × 1010
95-th percentile5.3067012 × 1010
Maximum5.6033 × 1010
Range5.503198 × 1010
Interquartile range (IQR)20063300

Descriptive statistics

Standard deviation1.5697544 × 109
Coefficient of variation (CV)0.029630815
Kurtosis751.92796
Mean5.2977092 × 1010
Median Absolute Deviation (MAD)27702
Skewness-26.966776
Sum8.836314 × 1015
Variance2.4641288 × 1018
MonotonicityNot monotonic
2024-03-11T03:41:35.047779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.30330282 × 10101914
 
1.1%
5.30330285 × 10101001
 
0.6%
5.303303232 × 1010763
 
0.5%
5.30330262 × 1010713
 
0.4%
5.30330093 × 1010647
 
0.4%
5.30670112 × 1010624
 
0.4%
5.303303232 × 1010564
 
0.3%
5.303303222 × 1010545
 
0.3%
5.303302501 × 1010532
 
0.3%
5.306105211 × 1010529
 
0.3%
Other values (2078) 158963
95.3%
ValueCountFrequency (%)
1001020100 2
< 0.1%
1081041901 1
< 0.1%
1097006803 1
< 0.1%
1117030352 1
< 0.1%
2020000206 1
< 0.1%
4013115900 1
< 0.1%
4013216901 1
< 0.1%
4013610301 1
< 0.1%
4013610302 1
< 0.1%
4013610500 1
< 0.1%
ValueCountFrequency (%)
5.60330001 × 10101
 
< 0.1%
5.307794001 × 10105
 
< 0.1%
5.307794001 × 10103
 
< 0.1%
5.307794 × 10102
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10107
 
< 0.1%
5.307794 × 10103
 
< 0.1%
5.30770034 × 101033
< 0.1%
5.30770032 × 101040
< 0.1%
5.30770031 × 101020
< 0.1%

Longitude
Real number (ℝ)

Distinct834
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.03183
Minimum-159.71248
Maximum-70.873801
Zeros0
Zeros (%)0.0%
Negative166795
Negative (%)100.0%
Memory size1.3 MiB
2024-03-11T03:41:35.370914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-159.71248
5-th percentile-122.87571
Q1-122.39418
median-122.29179
Q3-122.15166
95-th percentile-119.29521
Maximum-70.873801
Range88.838679
Interquartile range (IQR)0.24252

Descriptive statistics

Standard deviation1.8138196
Coefficient of variation (CV)-0.014863496
Kurtosis376.00813
Mean-122.03183
Median Absolute Deviation (MAD)0.13445
Skewness15.555343
Sum-20354299
Variance3.2899414
MonotonicityNot monotonic
2024-03-11T03:41:35.672489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.12302 4257
 
2.6%
-122.1873 3115
 
1.9%
-122.20264 2840
 
1.7%
-122.16937 2663
 
1.6%
-122.201905 2652
 
1.6%
-122.3185 2553
 
1.5%
-122.0313266 2341
 
1.4%
-122.151665 2312
 
1.4%
-122.29179 2286
 
1.4%
-122.209285 2237
 
1.3%
Other values (824) 139539
83.7%
ValueCountFrequency (%)
-159.71248 1
 
< 0.1%
-158.019325 1
 
< 0.1%
-158.009805 1
 
< 0.1%
-157.9124 3
 
< 0.1%
-157.74382 1
 
< 0.1%
-156.4531185 1
 
< 0.1%
-155.97182 1
 
< 0.1%
-149.5683 1
 
< 0.1%
-124.62514 8
< 0.1%
-124.3727994 3
 
< 0.1%
ValueCountFrequency (%)
-70.873801 1
< 0.1%
-70.930195 1
< 0.1%
-71.2733214 1
< 0.1%
-71.2785 1
< 0.1%
-71.30025 1
< 0.1%
-71.3487326 1
< 0.1%
-71.8380592 2
< 0.1%
-71.967835 1
< 0.1%
-72.1392432 1
< 0.1%
-72.27884 1
< 0.1%

Latitude
Real number (ℝ)

Distinct835
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.441833
Minimum19.61021
Maximum61.323155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2024-03-11T03:41:35.975980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19.61021
5-th percentile45.703706
Q147.357985
median47.610365
Q347.71558
95-th percentile48.21928
Maximum61.323155
Range41.712945
Interquartile range (IQR)0.357595

Descriptive statistics

Standard deviation0.82667522
Coefficient of variation (CV)0.017425027
Kurtosis165.34532
Mean47.441833
Median Absolute Deviation (MAD)0.162425
Skewness-8.5829475
Sum7913060.5
Variance0.68339192
MonotonicityNot monotonic
2024-03-11T03:41:36.302478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.67668 4257
 
2.6%
47.820245 3115
 
1.9%
47.6785 2840
 
1.7%
47.571015 2663
 
1.6%
47.61385 2652
 
1.6%
47.67949 2553
 
1.5%
47.6285782 2341
 
1.4%
47.75855 2312
 
1.4%
47.43473 2286
 
1.4%
47.71124 2237
 
1.3%
Other values (825) 139539
83.7%
ValueCountFrequency (%)
19.61021 1
 
< 0.1%
20.7761437 1
 
< 0.1%
21.33516 1
 
< 0.1%
21.343675 3
< 0.1%
21.39805 1
 
< 0.1%
21.455635 1
 
< 0.1%
21.969958 1
 
< 0.1%
25.765985 1
 
< 0.1%
26.257025 1
 
< 0.1%
26.6734075 1
 
< 0.1%
ValueCountFrequency (%)
61.323155 1
 
< 0.1%
48.99634 12
 
< 0.1%
48.99505 380
0.2%
48.9895316 1
 
< 0.1%
48.98823 53
 
< 0.1%
48.9492931 28
 
< 0.1%
48.9461196 217
0.1%
48.935165 16
 
< 0.1%
48.927085 8
 
< 0.1%
48.919121 25
 
< 0.1%

Interactions

2024-03-11T03:41:16.978642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:39:59.978379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:05.332728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:09.825613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:14.383189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:21.603962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:26.069028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:31.385119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:35.909153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:41.569240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:46.550158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:51.210571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:56.661161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:01.116868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:06.682617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:12.326857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:17.258289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:00.263572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:05.612550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:10.109640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:14.711418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:21.885593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:26.329376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:31.657541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:36.212817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:41.945724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:46.808648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:51.501770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:56.946568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:01.390673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:07.007792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:12.772329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:17.523401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:00.566381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:05.878753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:10.378237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:15.132143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:22.148879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:26.623791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:31.945766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:36.480231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:42.354154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:47.068900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:51.777339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:57.205500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:01.662512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-03-11T03:40:35.633293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:41.201402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:46.264003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:50.799617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:40:56.402877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:00.832646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:06.257215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:11.912881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-11T03:41:16.717601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-11T03:41:36.582878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2020 Census TractBase MSRPCityClean Alternative Fuel Vehicle EligibilityCountyDOL Vehicle IDElectric RangeElectric UtilityElectric Vehicle TypeLatitudeLegislative DistrictLongitudeMakeModelModel YearPostal CodeStateVIN (1-10)
2020 Census Tract1.0000.000-0.3650.009-0.3120.011-0.0150.4570.0130.215-0.1860.128-0.0160.0160.0110.0580.0800.009
Base MSRP0.0001.000-0.0050.024-0.002-0.0350.116-0.0000.0220.0060.011-0.0020.003-0.109-0.190-0.003-0.005-0.158
City-0.365-0.0051.0000.0440.608-0.000-0.020-0.2080.0650.1070.394-0.0040.0510.0410.028-0.3860.0790.045
Clean Alternative Fuel Vehicle Eligibility0.0090.0240.0441.0000.0120.092-0.7170.0220.743-0.027-0.0180.0170.067-0.0820.455-0.005-0.001-0.001
County-0.312-0.0020.6080.0121.0000.013-0.0520.1120.0990.1180.4340.3080.0660.0750.074-0.7460.0850.084
DOL Vehicle ID0.011-0.035-0.0000.0920.0131.000-0.1600.0260.078-0.018-0.0170.017-0.008-0.0600.348-0.0060.0120.069
Electric Range-0.0150.116-0.020-0.717-0.052-0.1601.000-0.0490.5250.005-0.004-0.047-0.113-0.127-0.6970.055-0.007-0.195
Electric Utility0.457-0.000-0.2080.0220.1120.026-0.0491.0000.0820.1540.0590.2180.0310.0700.067-0.4370.0880.070
Electric Vehicle Type0.0130.0220.0650.7430.0990.0780.5250.0821.000-0.054-0.068-0.056-0.296-0.489-0.1590.109-0.014-0.445
Latitude0.2150.0060.107-0.0270.118-0.0180.0050.154-0.0541.0000.2380.1430.0310.036-0.019-0.3240.0790.031
Legislative District-0.1860.0110.394-0.0180.434-0.017-0.0040.059-0.0680.2381.0000.0210.0480.036-0.014-0.337-0.0000.031
Longitude0.128-0.002-0.0040.0170.3080.017-0.0470.218-0.0560.1430.0211.0000.0620.0990.065-0.353-0.0680.090
Make-0.0160.0030.0510.0670.066-0.008-0.1130.031-0.2960.0310.0480.0621.0000.4940.092-0.091-0.0060.424
Model0.016-0.1090.041-0.0820.075-0.060-0.1270.070-0.4890.0360.0360.0990.4941.0000.064-0.1090.0020.822
Model Year0.011-0.1900.0280.4550.0740.348-0.6970.067-0.159-0.019-0.0140.0650.0920.0641.000-0.0610.0150.180
Postal Code0.058-0.003-0.386-0.005-0.746-0.0060.055-0.4370.109-0.324-0.337-0.353-0.091-0.109-0.0611.0000.079-0.105
State0.080-0.0050.079-0.0010.0850.012-0.0070.088-0.0140.079-0.000-0.068-0.0060.0020.0150.0791.0000.007
VIN (1-10)0.009-0.1580.045-0.0010.0840.069-0.1950.070-0.4450.0310.0310.0900.4240.8220.180-0.1050.0071.000

Missing values

2024-03-11T03:41:22.497858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-11T03:41:23.333159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDElectric Utility2020 Census TractLongitudeLatitude
089436393998902.0201498010087014.0159372112455.307700e+10-120.52401246.597394
14604240583998513.020173354280020002.0257167501988115.306701e+10-122.81754546.988760
258659446173998058.0202344311220011.0224071816988115.303303e+10-122.12988847.445126
3981957054403998012.02023301622010021.0260084653988115.306105e+10-122.18730047.820245
41708659426023998031.02020332892500322033.0253771913988115.303303e+10-122.20125247.393181
53555226553998370.02024423011039023.0259427829988115.303594e+10-122.64177047.737525
61155229883998367.02018728781033026.0477087012988115.303509e+10-122.68470747.505240
77555226553998370.020176641300238023.0214494213988115.303509e+10-122.64177047.737525
823155229883998366.02018332892500215026.0280785123988115.303509e+10-122.63926547.537300
93865945813998019.020184191900114045.0129133343988115.303303e+10-121.98107547.737796
VIN (1-10)CountyCityStatePostal CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDElectric Utility2020 Census TractLongitudeLatitude
166785386594278313998102.02024151751033043.0258782814296015.303301e+10-122.32226047.640580
16678618403927823998225.020241731611221042.0261031214988115.307300e+10-122.48611548.761615
16678714818426643998221.0201327132730075010.0259757998988115.305794e+10-122.61530548.501275
16678878659450283998033.0202229608010048.0199014224988115.303302e+10-122.20264047.678500
16678975597213998925.02022182043010013.0212130950988115.303798e+10-121.17616347.241060
16679013431225733999223.02013111778121906.0239527123324545.306300e+10-117.36970547.626370
1667914108659448763998074.020213332822010045.0148715479988115.303303e+10-122.03132747.628578
166792375195707373998275.020223332822010021.0220504406988115.306104e+10-122.29996547.941710
16679310709283998564.02013648251038020.0156418475324545.304197e+10-122.48753546.529013
166794591297218523998332.0201733761000210026.0169045789324545.305307e+10-122.58964547.342345